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共 206 条记录，以下是61-70 订阅

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Plant Disease Detection and Classification Using a Deep learning-Based Framework 24th

Plant Disease Detection and Classification Using a Deep Lear...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Ghosh, Mridul Lasker, Asifuzzaman Banerjee, Poushali Manna, Anindita Obaidullah, Sk Md Gonçalves, Teresa Roy, Kaushik Department of Computer Science ShyampurSiddheswariMahavidyalaya Howrah India Department of Computer Science and Engineering Aliah University Kolkata India Department of Computer Science Surendranath College Kolkata India Departamento de Informática Universidade de Évora Evora Portugal Department of Computer Science West Bengal State University Kolkata India

ISBN: (纸本)9783031482311

Plant diseases pose a significant threat to agriculture, causing substantial yield losses and economic damages worldwide. Traditional methods for detecting plant diseases are often time-consuming and require expert knowledge. In recent years, deep learning-based approaches have demonstrated great potential in the detection and classification of plant diseases. In this paper, we propose a Convolutional Neural Network (CNN) based framework for identifying 15 categories of plant leaf diseases, focusing on Tomato, Potato, and Bell pepper as the target plants. For our experiments, we utilized the publicly available PlantVillage dataset. the choice of a CNN for this task is justified by its recognition as one of the most popular and effective deep learning methods, especially for processing spatial data like images of plant leaves. We evaluated the performance of our model using various performance metrics, including accuracy, precision, recall, and F1-score. Our findings indicate that our approach outperforms state-of-the-art techniques, yielding encouraging results in terms of disease identification accuracy and classification precision. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： data handling

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Research on intelligent Driving Technology Based on Acoustic Vision Fusion 8

Research on Intelligent Driving Technology Based on Acoustic...

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8th international conference on Electromechanical Control Technology and Transportation, ICECTT 2023

作者： Pan, Junhao Ma, Zijing Feng, Xinyun School of Automobile and Transportation Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9781510668331

Aiming at the actual traffic driving environment, this paper proposes an acoustic perception method of intelligent vehicle based on microphone array, and combines the visual perception method of monocular camera to study the acoustic visual fusion perception method. the main research contents are as follows: Firstly, an acoustic simulation environment for intelligent vehicles is designed. Secondly, a sound source localization algorithm based on time delay estimation is proposed. Based on the geometry of the microphone array, the sound source localization problem is abstracted as a time delay estimation problem, and the sound source localization is realized. the received signal of the microphone is added by weighted delay to form a beam with a certain direction, which is visualized as a spatial SRP power map. the passive sound reception is transformed into an active search for space, which improves the positioning accuracy in low SNR and reverberation environment. thirdly, a sound source perception localization algorithm combining deep learning network and delay algorithm is developed. Based on SELDnet neural network, the SRP power map is used as input, and the cyclic convolution layer is replaced by the temporal convolution layer. the simulation results show that the positioning accuracy of the fusion positioning network is 65. 04 % higher than that of the SELDnet network in the ideal environment and 76. 19 % higher in the low SNR environment. the feasibility verification experiment and simulation experiment of the algorithm are carried out, and the sensor data in the real vehicle environment is collected for the real vehicle data verification experiment. the results show that the average positioning error of the acoustic vision fusion algorithm is 0. 1 m at the 60 m scale. At the same time, the algorithm also has strong adaptability to visual blind spots, which proves the deployment potential of the algorithm in the actual environment. © 2023 SPIE.

关键词： Convolution

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Modeling the Ink Tuning Process Using Machine learning 24th

Modeling the Ink Tuning Process Using Machine Learning

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Costa, Catarina Ferreira, Carlos Abreu Institute of Engineering Polytechnic Institute of Porto - ISEP/IPP Porto Portugal INESC TEC Porto Portugal

ISBN: (纸本)9783031482311

Paint bases are the essence of the color palette, allowing for the creation of a wide range of tones by combining them in different proportions. In this paper, an Artificial Neural Network is developed incorporating a pre-trained Decoder to predict the proportion of each paint base in an ink mixture in order to achieve the desired color. Color coordinates in the CIELAB space and the final finish are considered as input parameters. the proposed model is compared with commonly used models such as Linear Regression, Random Forest and Artificial Neural Network. It is important to note that the Artificial Neural Network was implemented with the same architecture as the proposed model but without incorporating the pre-trained Decoder. Experimental results demonstrate that the Artificial Neural Network with a pre-trained Decoder consistently outperforms the other models in predicting the proportions of paint bases for color tuning. this model exhibits lower Mean Absolute Error and Root Mean Square Error values across multiple objectives, indicating its superior accuracy in capturing the complexities of color relationships. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Paint

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A Machine learning Approach to Predict Cyclists’ Functional threshold Power 24th

A Machine Learning Approach to Predict Cyclists’ Functional...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Stockwell, Ronald Andrew Corradini, Andrea Oakville ON Canada MCI Universitätsstraße 15 Innsbruck6020 Austria

ISBN: (纸本)9783031482311

the estimated Functional threshold Power (eFTP) obtained from a cyclists best 20-min effort, expressed in watts, and multiplied by 95% is sometimes overstated, especially for non-elite level cyclists or cyclists just starting out. Since Functional threshold Power (FTP) is used for training prescription, in terms of creating power zones to train in, it is of utmost importance that the correct FTP is used. If FTP is overstated, a cyclist will overtrain at a level that is unrealistic to achieve. this research project aims at validating eFTP using a large, big data dataset, as well as to build out a supervised machine learning model, in the form of a weighted logistic regression, that can be used to predict FTP (pFTP), to create predictor of FTP that’s better than the currently accepted eFTP formula. Based on the data provided, our approach predicts FTP with far greater accuracy than eFTP, which turned out to overestimate cyclists’ FTP. the model outcome for each athlete, pFTP, was evaluated versus eFTP, by comparing each of these outcomes to the actual FTP (aFTP) observed in the data. To do this, Mean Squared Error and Mean Absolute Error were computed with pFTP producing values of 314.87 and 10.38 respectively when compared to aFTP, and eFTP producing 6417.32 and 61.16. With more accurate FTP estimations, cyclists are more likely to train in the correct power zones. It also validates that a laboratory environment is not always required when trying to validate eFTP and that a large, big data dataset can act as a valid substitute. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Mean square error

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Combining of Markov Random Field and Convolutional Neural Networks for Hyper/Multispectral Image Classification 24th

Combining of Markov Random Field and Convolutional Neural Ne...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Sahin, Halil Mertkan Grieve, Bruce Yin, Hujun Department of Electrical and Electronic Engineering The University of Manchester ManchesterM13 9PL United Kingdom

ISBN: (纸本)9783031482311

In the last decades, computer vision tasks such as face recognition, pattern classification, and object detection, have been extensively explored due to advances in computing technologies, learning algorithms, and availability of large amounts of labelled data. Image texture analysis is helpful and important to extracting valuable features of images in order to perform the aforementioned tasks. Markov Random Field (MRF) theory defines probabilistic relationships between the pixel value and its neighbouring pixels, which can help to produce features for classification purposes with deep learning. Convolutional Neural Network (CNN) is a popular feed-forward deep neural network, especially for handling visual tasks. therefore, this study focuses on combining MRF and CNN in order to achieve hyper/multispectral image classification tasks. MRF images were generated to produce prefixed MRF filters for the first and/or second attention-like layers of CNNs to better extract features. then CNNs were trained with the prefixed filters for experimentation on the UoM, BONN, and Cassava datasets. Experimental results have demonstrated that such combination of MRF and CNN can enhance classification accuracy while being more time-efficient for hyper/multispectral image classification compared to CNN structures without prefixed MRF filters. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image classification

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Glide Ratio Optimization for Wind Turbine Airfoils Based on Genetic Algorithms 24th

Glide Ratio Optimization for Wind Turbine Airfoils Based on ...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Radi, Jinane Djebli, Abdelouahed Sierra-Garcia, Jesús Enrique Santos, Matilde Energetic Laboratory Department of Physics Faculty of Sciences University of Abdelmalek Saadi Tetouan Tétouan Morocco Automation and Systems Engineering University of Burgos Burgos09006 Spain Institute of Knowledge Technology University Complutense of Madrid Madrid28040 Spain

ISBN: (纸本)9783031482311

the main objective in the design of wind turbine blades is the use of suitable airfoils to increase the aerodynamic performance and decrease the cost of energy. the objective of this research is to employ numerical optimization techniques to develop airfoils for wind turbines. To achieve this objective, a mathematical model is formulated by combining genetic algorithms with the XFOIL flow solver. three different airfoils with different characteristics are created using the genetic algorithm. throughout the optimization procedure, XFOIL software is used to determine the lift and drag coefficients. this study confirms the feasibility and effectiveness of the innovative design approach. Furthermore, it provides a valuable design concept that can be effectively applied to the airfoils of medium-thickness wind turbines. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Airfoils

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Language Models for Automatic Distribution of Review Notes in Movie Production 24th

Language Models for Automatic Distribution of Review Notes...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Garcés, Diego Santos, Matilde Fernández-Llorca, David Computer Science Faculty Complutense University of Madrid Madrid Spain Skydance Animation Madrid Madrid Spain Institute of Knowledge Technology Complutense University of Madrid Madrid Spain Computer Engineering Department University of Alcalá de Henares Madrid Spain European Commission Joint Research Centre Seville Spain

ISBN: (纸本)9783031482311

During the several years of production of an animated movie, review meetings take place daily, where supervisors and directors generate text notes about fixes needed for the movie. these notes are manually assigned to artistic departments for them to fixed. Being manual, many notes are not properly assigned and are never fixed, lowering the quality of the final movie. this paper presents a proposal for automating the distribution of these notes using multi-label text classification techniques. the comparison of the results obtained by fine-tuning several transformer-based language models is presented. A highest mean accuracy of 0.776 is achieved assigning several departments to each of the review notes in the test set with a BERT Multilingual model. A mean accuracy of 0.762 was reached in just 10 epochs and 10 min of training on an RTX-3090 with a DistilBERT transformer model. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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Application of Spatio-Temporal Big data Technology in Territorial Spatial Planning 8

Application of Spatio-Temporal Big Data Technology in Territ...

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8th international Technical conference on Frontiers of Hydraulic and Civil engineering Technology, HCET 2023

作者： Pu, Yu Li, Liang Li, Ruobing Ma, Zhongjian Cui, Di Xie, Wenpei Zhai, Hongting Zhang, Qingrui Shandong Urban and Rural Planning and Design Research Institute Co. Ltd. China Shandong University of Technology China Information & Telecommunications Company State Grid Shandong Electric Power Company China Golden Modern Information Industry Co. Ltd. China Integrated Electronic Systems Lab Co. Ltd. China Co. Ltd. China

ISBN: (纸本)9781643684642

Territorial spatial planning is the guide for national spatial development, the spatial blueprint for sustainable development, and the basic basis for various development, protection, and construction activities. With the development of machine learning and artificial intelligence, big data processing technology has also begun to integrate these technologies. these technologies can make big data processing more intelligent and automated, thereby providing more value for enterprises and individuals. Space-time big data technology is playing an increasingly important role in land planning. It is becoming a consensus to carry out 'accurate planning' by means of big data. However, how to obtain data and how to convert huge data into values and charts that can be used directly are still difficult problems. this paper takes the traffic planning research of Jinan City as an example. By continuously collecting traffic flow pictures of Internet maps for one month, using big data analysis technology to extract effective information, and using R language and ArcGIS to carry out time-space analysis, it provides theoretical support for the preparation of land spatial planning with multiple regulations. © 2023 the Authors.

关键词： Traffic congestion

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Internet of things in Agriculture Industry: Implementation, Applications, Challenges and Potential 8th

Internet of Things in Agriculture Industry: Implementation, ...

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8th international conference on Microelectronics, Circuits, and Systems, Micro 2021

作者： Singh, Kiran Jot Kapoor, Divneet Singh Sharma, Anshul thakur, Khushal Bajaj, Tanishq Tomar, Ashwin Mittal, Sparsh Singh, Baljap Agarwal, Raghav Embedded Systems and Robotics Research Group Chandigarh University Punjab Mohali140413 India

ISBN: (纸本)9789819904112

the Internet of things (IoT) is allowing agriculture, particularly arable farming, to become more data-driven, resulting in more timely and cost-effective agricultural production and management while reducing environmental impact. In comparison with other agricultural systems, this study would involve an empirical evaluation of current and prospective IoT applications in arable farming where spatial details, vastly diverse landscapes, mission variety, and mobile devices provide unique challenges to overcome. the analysis describes the state-of-the-art in terms of deployed technologies. It discusses present and future applications and provides an overview of current and possible applications, as well as problems, viable alternatives, and implementations. Finally, it explores any potential IoT applications in arable agriculture. Smart phones, intelligent sensor control, middleware platforms, integrated field management systems across the supply chain, and automated vehicles and robots all stand out as present issues preventing arable farming from becoming smart. Interoperability is a key obstacle across all layers of an IoT system’s design, which may be solved through shared standards and protocols, and it arises during implementation. the paper examined has identified and addressed in detail difficulties such as data privacy, cost, power consumption, network latency, data analytics, and security, among others. Different resolutions to all recognized challenges are presented by technologies such as artificial intelligence, intelligent data managing, and further addressed in depth issues such as availability, device power usage, latency of network, big data processing, data protection, and protection, among others. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Using Deep learning Models to Predict the Electrical Conductivity of the Influent in a Wastewater Treatment Plant 24th

Using Deep Learning Models to Predict the Electrical Condu...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Pereira, João Oliveira, Pedro Duarte, M. Salomé Martins, Gilberto Novais, Paulo LASI/ALGORITMI Centre University of Minho Braga Portugal CEB - Centre of Biological Engineering University of Minho Campus de Gualtar Braga4710-057 Portugal LABBELS - Associate Laboratory Braga/Guimarães Portugal

ISBN: (纸本)9783031482311

Nowadays, human population face increasing water pollution problems, so treating and managing this resource is crucial. Wastewater Treatment Plants (WWTPs) provide essential services for human life since they treat wastewater and monitor its parameters to preserve water quality standards. One of these parameters is electrical conductivity, essential in quantifying water salinity levels. therefore, this paper aims to forecast the influent conductivity in a WWTP for the next two timesteps. Hence, several experiments were conducted, considering the use of Transformers and Long Short-Term Memory (LSTMs) candidate models that were developed, tuned, and evaluated, utilising a recursive multi-step forecasting approach. the best candidate model was based on a Transformer architecture with encoding and obtained a RMSE of 155.2 μ S/cm. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Water pollution

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